Significance of HLA Mismatches in Poor Outcomes in Heart Transplant
Muhammad Sulman
Function to load data
load_data<-function(){
df<-read.csv("data2/data_clean.csv")
df$Time.upper.G1 <- ifelse(df$Time.lower.G1 == df$Time.upper.G1, Inf, df$Time.upper.G1)
df$Time.upper.G2 <- ifelse(df$Time.lower.G2 == df$Time.upper.G2, Inf, df$Time.upper.G2)
df$Time.upper.G3 <- ifelse(df$Time.lower.G3 == df$Time.upper.G3, Inf, df$Time.upper.G3)
df$Transplant.Date<-anydate(df$Transplant.Date)
df$Date.of.Graft.failure<-anydate(df$Date.of.Graft.failure)
df$DOB<-anydate(df$DOB)
df$age<-as.numeric(difftime(df$Transplant.Date, df$DOB, units="days"))/365.25
df<-df %>% filter(!df$Pre.DSAs=="Yes")
df$time2fail<-ifelse(is.na(df$Date.of.Graft.failure), anydate("2024-03-10")-df$Transplant.Date, df$Date.of.Graft.failure-df$Transplant.Date)
df$status<-ifelse(is.na(df$Date.of.Graft.failure), 0, 1)
df$tot_mismatch<-df$ClassI....Mismatch..All+df$ClassII....Mismatch..All
df$Donor.TGLN<-df$Donor.TGLN %>% as.character()
df$MRN<-df$MRN %>% as.character()
df$trans_id<-paste0(df$Donor.TGLN, df$MRN) %>% as.numeric()
df <- df %>% mutate(row_number = row_number())
df$DRB<-df$DRB1.Mismatch.No.+df$DRB345.Mismatch.No.
return(df)
}Independant variables in question for analysis are saved. So are HLA mismatch variables
HLA_vars<-c("A.Mismatch.No.", "B.Mismatch.No.", "C.Mismatch.No.", "DRB1.Mismatch.No.", "DRB345.Mismatch.No.", "DP.Mismatch.No.", "DQ.Mismatch.No.")
independent_vars<-c(HLA_vars, "age")
cumulative_cols<-c("ClassII....Mismatch..All", "ClassI....Mismatch..All", "tot_mismatch")
HLA_al_vars<-c("A", "B", "C", "DRB1", "DRB345", "DP", "DQ")
independent_al_vars<-c(HLA_al_vars, "age")load data and plot Kaplan-Meier curve
df<-load_data()## Warning in paste0(df$Donor.TGLN, df$MRN) %>% as.numeric(): NAs introduced by
## coercionplot(survfit(Surv(time2fail, status) ~ 1, data = df), xlab = "Time (days)", ylab = "Survival Probability", main = "Kaplan-Meier Estimate")
Subsetting df to make a correlation plot to check for possible confounding.
indep_df<-df[independent_vars]
indep_df%>%cor()%>%corrplot()
svg("allele_cor.svg")
indep_df_al<-df[independent_al_vars]
indep_df_al%>%cor()%>%corrplot()
dev.off()## png
## 2Log-rank tests and Kaplan-Meier curves
plot_quartiles_and_logrank <- function(data, independent_vars, status_var, time_var) {
results <- list()
for (var in independent_vars) {
data[var]<-ntile(data[var], 2)
fit_formula <- as.formula(paste0("Surv(", time_var, ", ", status_var, ") ~ ", var))
fit <- surv_fit(fit_formula, data = data)
p <- ggsurvplot(
fit,
data = data,
xlab = "Time",
ylab = "Survival probability",
palette = c("#4DAD5B","#FC4E07"),
conf.int = TRUE,
surv.scale = "percent",
)
log_rank_result <- survdiff(fit_formula, data = data)
# Store results
results[[var]] <- list(plot = p, log_rank_result = log_rank_result)
cat("Log-rank test result for", var, ":\n")
print(log_rank_result)
print(p)
#ggsave(paste0("figs/km_curve/", var, ".svg"))
}
return(results)
}Kaplan Meier curves and log-rank tests are performed for eplet mismatches
result<-plot_quartiles_and_logrank(df, independent_vars, "status", "time2fail")## Log-rank test result for A.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## A.Mismatch.No.=1 49 2 4.72 1.57 3.31
## A.Mismatch.No.=2 49 7 4.28 1.72 3.31
##
## Chisq= 3.3 on 1 degrees of freedom, p= 0.07
## Log-rank test result for B.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## B.Mismatch.No.=1 49 3 4.37 0.432 0.841
## B.Mismatch.No.=2 49 6 4.63 0.409 0.841
##
## Chisq= 0.8 on 1 degrees of freedom, p= 0.4
## Log-rank test result for C.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## C.Mismatch.No.=1 49 3 4.39 0.439 0.857
## C.Mismatch.No.=2 49 6 4.61 0.418 0.857
##
## Chisq= 0.9 on 1 degrees of freedom, p= 0.4
## Log-rank test result for DRB1.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DRB1.Mismatch.No.=1 49 4 4.5 0.0549 0.11
## DRB1.Mismatch.No.=2 49 5 4.5 0.0549 0.11
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.7
## Log-rank test result for DRB345.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DRB345.Mismatch.No.=1 49 6 4.44 0.550 1.09
## DRB345.Mismatch.No.=2 49 3 4.56 0.535 1.09
##
## Chisq= 1.1 on 1 degrees of freedom, p= 0.3
## Log-rank test result for DP.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DP.Mismatch.No.=1 49 4 4.38 0.0337 0.0659
## DP.Mismatch.No.=2 49 5 4.62 0.0321 0.0659
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.8
## Log-rank test result for DQ.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DQ.Mismatch.No.=1 49 6 4.55 0.464 0.94
## DQ.Mismatch.No.=2 49 3 4.45 0.474 0.94
##
## Chisq= 0.9 on 1 degrees of freedom, p= 0.3
## Log-rank test result for age :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## age=1 49 4 4.49 0.0533 0.106
## age=2 49 5 4.51 0.0530 0.106
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.7
result2<-plot_quartiles_and_logrank(df, cumulative_cols, "status", "time2fail")## Log-rank test result for ClassII....Mismatch..All :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## ClassII....Mismatch..All=1 49 7 4.43 1.49 2.94
## ClassII....Mismatch..All=2 49 2 4.57 1.45 2.94
##
## Chisq= 2.9 on 1 degrees of freedom, p= 0.09
## Log-rank test result for ClassI....Mismatch..All :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## ClassI....Mismatch..All=1 49 2 4.49 1.38 2.76
## ClassI....Mismatch..All=2 49 7 4.51 1.38 2.76
##
## Chisq= 2.8 on 1 degrees of freedom, p= 0.1
## Log-rank test result for tot_mismatch :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## tot_mismatch=1 49 5 4.39 0.0856 0.167
## tot_mismatch=2 49 4 4.61 0.0814 0.167
##
## Chisq= 0.2 on 1 degrees of freedom, p= 0.7
Kaplan Meier curves and log-rank tests are performed for allele mismatches
result<-plot_quartiles_and_logrank(df, independent_al_vars, "status", "time2fail")## Log-rank test result for A :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## A=1 49 6 4.18 0.796 1.49
## A=2 49 3 4.82 0.689 1.49
##
## Chisq= 1.5 on 1 degrees of freedom, p= 0.2
## Log-rank test result for B :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## B=1 49 6 3.94 1.08 1.98
## B=2 49 3 5.06 0.84 1.98
##
## Chisq= 2 on 1 degrees of freedom, p= 0.2
## Log-rank test result for C :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## C=1 49 3 4.37 0.432 0.84
## C=2 49 6 4.63 0.408 0.84
##
## Chisq= 0.8 on 1 degrees of freedom, p= 0.4
## Log-rank test result for DRB1 :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DRB1=1 49 4 4.35 0.0280 0.0543
## DRB1=2 49 5 4.65 0.0262 0.0543
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.8
## Log-rank test result for DRB345 :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DRB345=1 49 4 4.73 0.112 0.236
## DRB345=2 49 5 4.27 0.124 0.236
##
## Chisq= 0.2 on 1 degrees of freedom, p= 0.6
## Log-rank test result for DP :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DP=1 49 4 4.12 0.00370 0.0069
## DP=2 49 5 4.88 0.00313 0.0069
##
## Chisq= 0 on 1 degrees of freedom, p= 0.9
## Log-rank test result for DQ :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DQ=1 49 6 4.55 0.459 0.932
## DQ=2 49 3 4.45 0.470 0.932
##
## Chisq= 0.9 on 1 degrees of freedom, p= 0.3
## Log-rank test result for age :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## age=1 49 4 4.49 0.0533 0.106
## age=2 49 5 4.51 0.0530 0.106
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.7
Cox proportional hazards univariate modeling function
univariate_coxph <- function(data, independent_vars, status_var, time_var) {
results <- list()
summary_table <- data.frame(Variable = character(),
HR = numeric(),
Lower_CI = numeric(),
Upper_CI = numeric(),
p_value = numeric(),
C_index = numeric(),
stringsAsFactors = FALSE)
for (var in independent_vars) {
fit_formula <- as.formula(paste0("Surv(", time_var, ", ", status_var, ") ~ ", var))
fit <- coxph(fit_formula, data = data, na.action = na.exclude) # Exclude NA values
p<-ggcoxzph(cox.zph(fit))
print(p)
# Extract summary information
cox_summary <- summary(fit)
hr <- exp(coef(fit))
ci <- exp(confint(fit))
p_val <- cox_summary$coefficients[,5]
c_index <- cox_summary$concordance[1] # Extract C-index
# Print model summary for each variable
cat("\nCox proportional hazards model for", var, ":\n")
print(cox_summary)
# Store results in summary table
summary_table <- rbind(summary_table, data.frame(Variable = var,
HR = hr,
Lower_CI = ci[1],
Upper_CI = ci[2],
p_value = p_val,
C_index = c_index))
results[[var]] <- list(Model = fit, Summary = cox_summary)
}
# Print summary table of key results
print(summary_table)
return(results)
}Univariate coxph models for eplet time to failure
cox_results<-univariate_coxph(df, independent_vars, "status", "time2fail")
##
## Cox proportional hazards model for A.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## A.Mismatch.No. 0.06325 1.06529 0.04828 1.31 0.19
##
## exp(coef) exp(-coef) lower .95 upper .95
## A.Mismatch.No. 1.065 0.9387 0.9691 1.171
##
## Concordance= 0.682 (se = 0.1 )
## Likelihood ratio test= 1.69 on 1 df, p=0.2
## Wald test = 1.72 on 1 df, p=0.2
## Score (logrank) test = 1.71 on 1 df, p=0.2
##
## Cox proportional hazards model for B.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## B.Mismatch.No. 0.01369 1.01378 0.05469 0.25 0.802
##
## exp(coef) exp(-coef) lower .95 upper .95
## B.Mismatch.No. 1.014 0.9864 0.9107 1.128
##
## Concordance= 0.525 (se = 0.096 )
## Likelihood ratio test= 0.06 on 1 df, p=0.8
## Wald test = 0.06 on 1 df, p=0.8
## Score (logrank) test = 0.06 on 1 df, p=0.8
##
## Cox proportional hazards model for C.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## C.Mismatch.No. 0.09009 1.09427 0.05779 1.559 0.119
##
## exp(coef) exp(-coef) lower .95 upper .95
## C.Mismatch.No. 1.094 0.9139 0.9771 1.226
##
## Concordance= 0.656 (se = 0.076 )
## Likelihood ratio test= 2.28 on 1 df, p=0.1
## Wald test = 2.43 on 1 df, p=0.1
## Score (logrank) test = 2.44 on 1 df, p=0.1
##
## Cox proportional hazards model for DRB1.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB1.Mismatch.No. 0.0006791 1.0006793 0.0527482 0.013 0.99
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB1.Mismatch.No. 1.001 0.9993 0.9024 1.11
##
## Concordance= 0.549 (se = 0.078 )
## Likelihood ratio test= 0 on 1 df, p=1
## Wald test = 0 on 1 df, p=1
## Score (logrank) test = 0 on 1 df, p=1
##
## Cox proportional hazards model for DRB345.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB345.Mismatch.No. -0.06852 0.93378 0.04904 -1.397 0.162
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB345.Mismatch.No. 0.9338 1.071 0.8482 1.028
##
## Concordance= 0.593 (se = 0.09 )
## Likelihood ratio test= 2.25 on 1 df, p=0.1
## Wald test = 1.95 on 1 df, p=0.2
## Score (logrank) test = 1.98 on 1 df, p=0.2
##
## Cox proportional hazards model for DP.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DP.Mismatch.No. -0.001622 0.998379 0.046560 -0.035 0.972
##
## exp(coef) exp(-coef) lower .95 upper .95
## DP.Mismatch.No. 0.9984 1.002 0.9113 1.094
##
## Concordance= 0.544 (se = 0.096 )
## Likelihood ratio test= 0 on 1 df, p=1
## Wald test = 0 on 1 df, p=1
## Score (logrank) test = 0 on 1 df, p=1
##
## Cox proportional hazards model for DQ.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DQ.Mismatch.No. -0.02535 0.97497 0.05212 -0.486 0.627
##
## exp(coef) exp(-coef) lower .95 upper .95
## DQ.Mismatch.No. 0.975 1.026 0.8803 1.08
##
## Concordance= 0.53 (se = 0.063 )
## Likelihood ratio test= 0.24 on 1 df, p=0.6
## Wald test = 0.24 on 1 df, p=0.6
## Score (logrank) test = 0.24 on 1 df, p=0.6
##
## Cox proportional hazards model for age :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## age -0.002506 0.997497 0.029763 -0.084 0.933
##
## exp(coef) exp(-coef) lower .95 upper .95
## age 0.9975 1.003 0.941 1.057
##
## Concordance= 0.477 (se = 0.111 )
## Likelihood ratio test= 0.01 on 1 df, p=0.9
## Wald test = 0.01 on 1 df, p=0.9
## Score (logrank) test = 0.01 on 1 df, p=0.9
##
## Variable HR Lower_CI Upper_CI p_value
## A.Mismatch.No. A.Mismatch.No. 1.0652930 0.9691025 1.171031 0.1902104
## B.Mismatch.No. B.Mismatch.No. 1.0137822 0.9107412 1.128481 0.8023563
## C.Mismatch.No. C.Mismatch.No. 1.0942703 0.9770878 1.225507 0.1190252
## DRB1.Mismatch.No. DRB1.Mismatch.No. 1.0006793 0.9023926 1.109671 0.9897287
## DRB345.Mismatch.No. DRB345.Mismatch.No. 0.9337766 0.8482019 1.027985 0.1623660
## DP.Mismatch.No. DP.Mismatch.No. 0.9983792 0.9113051 1.093773 0.9722078
## DQ.Mismatch.No. DQ.Mismatch.No. 0.9749681 0.8802975 1.079820 0.6266642
## age age 0.9974973 0.9409729 1.057417 0.9329038
## C_index
## A.Mismatch.No. 0.6818774
## B.Mismatch.No. 0.5247718
## C.Mismatch.No. 0.6558018
## DRB1.Mismatch.No. 0.5488918
## DRB345.Mismatch.No. 0.5932203
## DP.Mismatch.No. 0.5436767
## DQ.Mismatch.No. 0.5299870
## age 0.4771838cox_results## $A.Mismatch.No.
## $A.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## A.Mismatch.No. 0.06325 1.06529 0.04828 1.31 0.19
##
## Likelihood ratio test=1.69 on 1 df, p=0.1939
## n= 98, number of events= 9
##
## $A.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## A.Mismatch.No. 0.06325 1.06529 0.04828 1.31 0.19
##
## exp(coef) exp(-coef) lower .95 upper .95
## A.Mismatch.No. 1.065 0.9387 0.9691 1.171
##
## Concordance= 0.682 (se = 0.1 )
## Likelihood ratio test= 1.69 on 1 df, p=0.2
## Wald test = 1.72 on 1 df, p=0.2
## Score (logrank) test = 1.71 on 1 df, p=0.2
##
##
##
## $B.Mismatch.No.
## $B.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## B.Mismatch.No. 0.01369 1.01378 0.05469 0.25 0.802
##
## Likelihood ratio test=0.06 on 1 df, p=0.8028
## n= 98, number of events= 9
##
## $B.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## B.Mismatch.No. 0.01369 1.01378 0.05469 0.25 0.802
##
## exp(coef) exp(-coef) lower .95 upper .95
## B.Mismatch.No. 1.014 0.9864 0.9107 1.128
##
## Concordance= 0.525 (se = 0.096 )
## Likelihood ratio test= 0.06 on 1 df, p=0.8
## Wald test = 0.06 on 1 df, p=0.8
## Score (logrank) test = 0.06 on 1 df, p=0.8
##
##
##
## $C.Mismatch.No.
## $C.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## C.Mismatch.No. 0.09009 1.09427 0.05779 1.559 0.119
##
## Likelihood ratio test=2.28 on 1 df, p=0.1308
## n= 98, number of events= 9
##
## $C.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## C.Mismatch.No. 0.09009 1.09427 0.05779 1.559 0.119
##
## exp(coef) exp(-coef) lower .95 upper .95
## C.Mismatch.No. 1.094 0.9139 0.9771 1.226
##
## Concordance= 0.656 (se = 0.076 )
## Likelihood ratio test= 2.28 on 1 df, p=0.1
## Wald test = 2.43 on 1 df, p=0.1
## Score (logrank) test = 2.44 on 1 df, p=0.1
##
##
##
## $DRB1.Mismatch.No.
## $DRB1.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## DRB1.Mismatch.No. 0.0006791 1.0006793 0.0527482 0.013 0.99
##
## Likelihood ratio test=0 on 1 df, p=0.9897
## n= 98, number of events= 9
##
## $DRB1.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB1.Mismatch.No. 0.0006791 1.0006793 0.0527482 0.013 0.99
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB1.Mismatch.No. 1.001 0.9993 0.9024 1.11
##
## Concordance= 0.549 (se = 0.078 )
## Likelihood ratio test= 0 on 1 df, p=1
## Wald test = 0 on 1 df, p=1
## Score (logrank) test = 0 on 1 df, p=1
##
##
##
## $DRB345.Mismatch.No.
## $DRB345.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## DRB345.Mismatch.No. -0.06852 0.93378 0.04904 -1.397 0.162
##
## Likelihood ratio test=2.25 on 1 df, p=0.1332
## n= 98, number of events= 9
##
## $DRB345.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB345.Mismatch.No. -0.06852 0.93378 0.04904 -1.397 0.162
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB345.Mismatch.No. 0.9338 1.071 0.8482 1.028
##
## Concordance= 0.593 (se = 0.09 )
## Likelihood ratio test= 2.25 on 1 df, p=0.1
## Wald test = 1.95 on 1 df, p=0.2
## Score (logrank) test = 1.98 on 1 df, p=0.2
##
##
##
## $DP.Mismatch.No.
## $DP.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## DP.Mismatch.No. -0.001622 0.998379 0.046560 -0.035 0.972
##
## Likelihood ratio test=0 on 1 df, p=0.9722
## n= 98, number of events= 9
##
## $DP.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DP.Mismatch.No. -0.001622 0.998379 0.046560 -0.035 0.972
##
## exp(coef) exp(-coef) lower .95 upper .95
## DP.Mismatch.No. 0.9984 1.002 0.9113 1.094
##
## Concordance= 0.544 (se = 0.096 )
## Likelihood ratio test= 0 on 1 df, p=1
## Wald test = 0 on 1 df, p=1
## Score (logrank) test = 0 on 1 df, p=1
##
##
##
## $DQ.Mismatch.No.
## $DQ.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## DQ.Mismatch.No. -0.02535 0.97497 0.05212 -0.486 0.627
##
## Likelihood ratio test=0.24 on 1 df, p=0.6235
## n= 98, number of events= 9
##
## $DQ.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DQ.Mismatch.No. -0.02535 0.97497 0.05212 -0.486 0.627
##
## exp(coef) exp(-coef) lower .95 upper .95
## DQ.Mismatch.No. 0.975 1.026 0.8803 1.08
##
## Concordance= 0.53 (se = 0.063 )
## Likelihood ratio test= 0.24 on 1 df, p=0.6
## Wald test = 0.24 on 1 df, p=0.6
## Score (logrank) test = 0.24 on 1 df, p=0.6
##
##
##
## $age
## $age$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## age -0.002506 0.997497 0.029763 -0.084 0.933
##
## Likelihood ratio test=0.01 on 1 df, p=0.9333
## n= 98, number of events= 9
##
## $age$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## age -0.002506 0.997497 0.029763 -0.084 0.933
##
## exp(coef) exp(-coef) lower .95 upper .95
## age 0.9975 1.003 0.941 1.057
##
## Concordance= 0.477 (se = 0.111 )
## Likelihood ratio test= 0.01 on 1 df, p=0.9
## Wald test = 0.01 on 1 df, p=0.9
## Score (logrank) test = 0.01 on 1 df, p=0.9cox_results2<-univariate_coxph(df, cumulative_cols, "status", "time2fail")
##
## Cox proportional hazards model for ClassII....Mismatch..All :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## ClassII....Mismatch..All -0.01650 0.98364 0.01987 -0.83 0.406
##
## exp(coef) exp(-coef) lower .95 upper .95
## ClassII....Mismatch..All 0.9836 1.017 0.9461 1.023
##
## Concordance= 0.587 (se = 0.07 )
## Likelihood ratio test= 0.73 on 1 df, p=0.4
## Wald test = 0.69 on 1 df, p=0.4
## Score (logrank) test = 0.68 on 1 df, p=0.4
##
## Cox proportional hazards model for ClassI....Mismatch..All :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## ClassI....Mismatch..All 0.03668 1.03736 0.02465 1.488 0.137
##
## exp(coef) exp(-coef) lower .95 upper .95
## ClassI....Mismatch..All 1.037 0.964 0.9884 1.089
##
## Concordance= 0.673 (se = 0.096 )
## Likelihood ratio test= 2.09 on 1 df, p=0.1
## Wald test = 2.21 on 1 df, p=0.1
## Score (logrank) test = 2.2 on 1 df, p=0.1
##
## Cox proportional hazards model for tot_mismatch :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## tot_mismatch 0.002003 1.002005 0.014023 0.143 0.886
##
## exp(coef) exp(-coef) lower .95 upper .95
## tot_mismatch 1.002 0.998 0.9748 1.03
##
## Concordance= 0.518 (se = 0.081 )
## Likelihood ratio test= 0.02 on 1 df, p=0.9
## Wald test = 0.02 on 1 df, p=0.9
## Score (logrank) test = 0.02 on 1 df, p=0.9
##
## Variable HR Lower_CI Upper_CI
## ClassII....Mismatch..All ClassII....Mismatch..All 0.9836383 0.9460761 1.022692
## ClassI....Mismatch..All ClassI....Mismatch..All 1.0373620 0.9884388 1.088707
## tot_mismatch tot_mismatch 1.0020053 0.9748400 1.029927
## p_value C_index
## ClassII....Mismatch..All 0.4062866 0.5873533
## ClassI....Mismatch..All 0.1367029 0.6734029
## tot_mismatch 0.8864073 0.5182529cox_results2## $ClassII....Mismatch..All
## $ClassII....Mismatch..All$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## ClassII....Mismatch..All -0.01650 0.98364 0.01987 -0.83 0.406
##
## Likelihood ratio test=0.73 on 1 df, p=0.3939
## n= 98, number of events= 9
##
## $ClassII....Mismatch..All$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## ClassII....Mismatch..All -0.01650 0.98364 0.01987 -0.83 0.406
##
## exp(coef) exp(-coef) lower .95 upper .95
## ClassII....Mismatch..All 0.9836 1.017 0.9461 1.023
##
## Concordance= 0.587 (se = 0.07 )
## Likelihood ratio test= 0.73 on 1 df, p=0.4
## Wald test = 0.69 on 1 df, p=0.4
## Score (logrank) test = 0.68 on 1 df, p=0.4
##
##
##
## $ClassI....Mismatch..All
## $ClassI....Mismatch..All$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## ClassI....Mismatch..All 0.03668 1.03736 0.02465 1.488 0.137
##
## Likelihood ratio test=2.09 on 1 df, p=0.148
## n= 98, number of events= 9
##
## $ClassI....Mismatch..All$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## ClassI....Mismatch..All 0.03668 1.03736 0.02465 1.488 0.137
##
## exp(coef) exp(-coef) lower .95 upper .95
## ClassI....Mismatch..All 1.037 0.964 0.9884 1.089
##
## Concordance= 0.673 (se = 0.096 )
## Likelihood ratio test= 2.09 on 1 df, p=0.1
## Wald test = 2.21 on 1 df, p=0.1
## Score (logrank) test = 2.2 on 1 df, p=0.1
##
##
##
## $tot_mismatch
## $tot_mismatch$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## tot_mismatch 0.002003 1.002005 0.014023 0.143 0.886
##
## Likelihood ratio test=0.02 on 1 df, p=0.8866
## n= 98, number of events= 9
##
## $tot_mismatch$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## tot_mismatch 0.002003 1.002005 0.014023 0.143 0.886
##
## exp(coef) exp(-coef) lower .95 upper .95
## tot_mismatch 1.002 0.998 0.9748 1.03
##
## Concordance= 0.518 (se = 0.081 )
## Likelihood ratio test= 0.02 on 1 df, p=0.9
## Wald test = 0.02 on 1 df, p=0.9
## Score (logrank) test = 0.02 on 1 df, p=0.9Time to failure for antigen MM
cox_results<-univariate_coxph(df, independent_al_vars, "status", "time2fail")##
## Cox proportional hazards model for A :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## A -0.3304 0.7186 0.6660 -0.496 0.62
##
## exp(coef) exp(-coef) lower .95 upper .95
## A 0.7186 1.392 0.1948 2.651
##
## Concordance= 0.493 (se = 0.092 )
## Likelihood ratio test= 0.24 on 1 df, p=0.6
## Wald test = 0.25 on 1 df, p=0.6
## Score (logrank) test = 0.25 on 1 df, p=0.6## Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; coefficient may be infinite.
##
## Cox proportional hazards model for B :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## B 1.936e+01 2.565e+08 1.064e+04 0.002 0.999
##
## exp(coef) exp(-coef) lower .95 upper .95
## B 256543565 3.898e-09 0 Inf
##
## Concordance= 0.602 (se = 0.021 )
## Likelihood ratio test= 4.04 on 1 df, p=0.04
## Wald test = 0 on 1 df, p=1
## Score (logrank) test = 2.27 on 1 df, p=0.1
##
## Cox proportional hazards model for C :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## C 1.0649 2.9005 0.7584 1.404 0.16
##
## exp(coef) exp(-coef) lower .95 upper .95
## C 2.9 0.3448 0.656 12.82
##
## Concordance= 0.646 (se = 0.062 )
## Likelihood ratio test= 2.54 on 1 df, p=0.1
## Wald test = 1.97 on 1 df, p=0.2
## Score (logrank) test = 2.15 on 1 df, p=0.1
##
## Cox proportional hazards model for DRB1 :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB1 0.9040 2.4694 0.7626 1.185 0.236
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB1 2.469 0.405 0.5539 11.01
##
## Concordance= 0.619 (se = 0.063 )
## Likelihood ratio test= 1.74 on 1 df, p=0.2
## Wald test = 1.41 on 1 df, p=0.2
## Score (logrank) test = 1.5 on 1 df, p=0.2
##
## Cox proportional hazards model for DRB345 :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB345 -0.02847 0.97193 0.18092 -0.157 0.875
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB345 0.9719 1.029 0.6818 1.386
##
## Concordance= 0.481 (se = 0.08 )
## Likelihood ratio test= 0.02 on 1 df, p=0.9
## Wald test = 0.02 on 1 df, p=0.9
## Score (logrank) test = 0.02 on 1 df, p=0.9
##
## Cox proportional hazards model for DP :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DP 0.5567 1.7449 0.3350 1.662 0.0966 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## DP 1.745 0.5731 0.9049 3.365
##
## Concordance= 0.614 (se = 0.094 )
## Likelihood ratio test= 3.03 on 1 df, p=0.08
## Wald test = 2.76 on 1 df, p=0.1
## Score (logrank) test = 2.84 on 1 df, p=0.09
##
## Cox proportional hazards model for DQ :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DQ 0.2210 1.2473 0.3252 0.68 0.497
##
## exp(coef) exp(-coef) lower .95 upper .95
## DQ 1.247 0.8017 0.6594 2.359
##
## Concordance= 0.561 (se = 0.081 )
## Likelihood ratio test= 0.48 on 1 df, p=0.5
## Wald test = 0.46 on 1 df, p=0.5
## Score (logrank) test = 0.47 on 1 df, p=0.5
##
## Cox proportional hazards model for age :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 9
##
## coef exp(coef) se(coef) z Pr(>|z|)
## age -0.002506 0.997497 0.029763 -0.084 0.933
##
## exp(coef) exp(-coef) lower .95 upper .95
## age 0.9975 1.003 0.941 1.057
##
## Concordance= 0.477 (se = 0.111 )
## Likelihood ratio test= 0.01 on 1 df, p=0.9
## Wald test = 0.01 on 1 df, p=0.9
## Score (logrank) test = 0.01 on 1 df, p=0.9
##
## Variable HR Lower_CI Upper_CI p_value C_index
## A A 7.186378e-01 0.1947981 2.651156 0.61984442 0.4928292
## B B 2.565436e+08 0.0000000 Inf 0.99854768 0.6016949
## C C 2.900495e+00 0.6559927 12.824645 0.16029853 0.6460235
## DRB1 DRB1 2.469357e+00 0.5539330 11.008053 0.23587424 0.6186441
## DRB345 DRB345 9.719275e-01 0.6817626 1.385590 0.87494277 0.4810952
## DP DP 1.744910e+00 0.9049047 3.364676 0.09657353 0.6140808
## DQ DQ 1.247328e+00 0.6594318 2.359344 0.49676253 0.5612777
## age age 9.974973e-01 0.9409729 1.057417 0.93290381 0.4771838Grade 1 coxph eplet
cox_results<-univariate_coxph(df, independent_vars, "Status.G1", "Med.Time.G1")## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside##
## Cox proportional hazards model for A.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## A.Mismatch.No. 0.00244 1.00244 0.01379 0.177 0.86
##
## exp(coef) exp(-coef) lower .95 upper .95
## A.Mismatch.No. 1.002 0.9976 0.9757 1.03
##
## Concordance= 0.527 (se = 0.047 )
## Likelihood ratio test= 0.03 on 1 df, p=0.9
## Wald test = 0.03 on 1 df, p=0.9
## Score (logrank) test = 0.03 on 1 df, p=0.9## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for B.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## B.Mismatch.No. 0.02007 1.02028 0.01742 1.152 0.249
##
## exp(coef) exp(-coef) lower .95 upper .95
## B.Mismatch.No. 1.02 0.9801 0.986 1.056
##
## Concordance= 0.547 (se = 0.044 )
## Likelihood ratio test= 1.32 on 1 df, p=0.3
## Wald test = 1.33 on 1 df, p=0.2
## Score (logrank) test = 1.33 on 1 df, p=0.2## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for C.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## C.Mismatch.No. -0.01988 0.98032 0.02019 -0.985 0.325
##
## exp(coef) exp(-coef) lower .95 upper .95
## C.Mismatch.No. 0.9803 1.02 0.9423 1.02
##
## Concordance= 0.56 (se = 0.047 )
## Likelihood ratio test= 0.98 on 1 df, p=0.3
## Wald test = 0.97 on 1 df, p=0.3
## Score (logrank) test = 0.97 on 1 df, p=0.3## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for DRB1.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB1.Mismatch.No. -0.01522 0.98490 0.01537 -0.99 0.322
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB1.Mismatch.No. 0.9849 1.015 0.9557 1.015
##
## Concordance= 0.506 (se = 0.047 )
## Likelihood ratio test= 0.98 on 1 df, p=0.3
## Wald test = 0.98 on 1 df, p=0.3
## Score (logrank) test = 0.98 on 1 df, p=0.3## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for DRB345.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB345.Mismatch.No. -0.03434 0.96625 0.01359 -2.527 0.0115 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB345.Mismatch.No. 0.9662 1.035 0.9409 0.9923
##
## Concordance= 0.602 (se = 0.045 )
## Likelihood ratio test= 6.74 on 1 df, p=0.009
## Wald test = 6.39 on 1 df, p=0.01
## Score (logrank) test = 6.36 on 1 df, p=0.01## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for DP.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DP.Mismatch.No. -0.006597 0.993425 0.014647 -0.45 0.652
##
## exp(coef) exp(-coef) lower .95 upper .95
## DP.Mismatch.No. 0.9934 1.007 0.9653 1.022
##
## Concordance= 0.545 (se = 0.049 )
## Likelihood ratio test= 0.2 on 1 df, p=0.7
## Wald test = 0.2 on 1 df, p=0.7
## Score (logrank) test = 0.2 on 1 df, p=0.7## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for DQ.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DQ.Mismatch.No. 0.01773 1.01789 0.01536 1.155 0.248
##
## exp(coef) exp(-coef) lower .95 upper .95
## DQ.Mismatch.No. 1.018 0.9824 0.9877 1.049
##
## Concordance= 0.585 (se = 0.041 )
## Likelihood ratio test= 1.31 on 1 df, p=0.3
## Wald test = 1.33 on 1 df, p=0.2
## Score (logrank) test = 1.33 on 1 df, p=0.2## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for age :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## age -0.03635 0.96430 0.00961 -3.783 0.000155 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## age 0.9643 1.037 0.9463 0.9826
##
## Concordance= 0.638 (se = 0.038 )
## Likelihood ratio test= 12.55 on 1 df, p=4e-04
## Wald test = 14.31 on 1 df, p=2e-04
## Score (logrank) test = 14.54 on 1 df, p=1e-04
##
## Variable HR Lower_CI Upper_CI
## A.Mismatch.No. A.Mismatch.No. 1.0024427 0.9757186 1.029899
## B.Mismatch.No. B.Mismatch.No. 1.0202758 0.9860249 1.055717
## C.Mismatch.No. C.Mismatch.No. 0.9803180 0.9422908 1.019880
## DRB1.Mismatch.No. DRB1.Mismatch.No. 0.9848956 0.9556609 1.015025
## DRB345.Mismatch.No. DRB345.Mismatch.No. 0.9662456 0.9408553 0.992321
## DP.Mismatch.No. DP.Mismatch.No. 0.9934252 0.9653119 1.022357
## DQ.Mismatch.No. DQ.Mismatch.No. 1.0178887 0.9877095 1.048990
## age age 0.9643023 0.9463096 0.982637
## p_value C_index
## A.Mismatch.No. 0.8595337966 0.5266052
## B.Mismatch.No. 0.2492568889 0.5466706
## C.Mismatch.No. 0.3247370730 0.5600476
## DRB1.Mismatch.No. 0.3221961439 0.5059453
## DRB345.Mismatch.No. 0.0114928267 0.6015161
## DP.Mismatch.No. 0.6524459200 0.5447384
## DQ.Mismatch.No. 0.2482394454 0.5847206
## age 0.0001551797 0.6382283cox_multi<-coxph(Surv(Med.Time.G1, Status.G1) ~ A.Mismatch.No. + B.Mismatch.No. + C.Mismatch.No. + DRB1.Mismatch.No. + DRB345.Mismatch.No. + DP.Mismatch.No. + DQ.Mismatch.No. + age, data=df)
summary(cox_multi)## Call:
## coxph(formula = Surv(Med.Time.G1, Status.G1) ~ A.Mismatch.No. +
## B.Mismatch.No. + C.Mismatch.No. + DRB1.Mismatch.No. + DRB345.Mismatch.No. +
## DP.Mismatch.No. + DQ.Mismatch.No. + age, data = df)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## A.Mismatch.No. 0.005557 1.005573 0.016575 0.335 0.73742
## B.Mismatch.No. 0.015307 1.015425 0.019590 0.781 0.43460
## C.Mismatch.No. -0.034148 0.966428 0.024514 -1.393 0.16362
## DRB1.Mismatch.No. 0.007341 1.007368 0.020268 0.362 0.71720
## DRB345.Mismatch.No. -0.043407 0.957522 0.016613 -2.613 0.00898 **
## DP.Mismatch.No. -0.018227 0.981938 0.015131 -1.205 0.22835
## DQ.Mismatch.No. 0.031224 1.031716 0.018178 1.718 0.08586 .
## age -0.031778 0.968722 0.010238 -3.104 0.00191 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## A.Mismatch.No. 1.0056 0.9945 0.9734 1.0388
## B.Mismatch.No. 1.0154 0.9848 0.9772 1.0552
## C.Mismatch.No. 0.9664 1.0347 0.9211 1.0140
## DRB1.Mismatch.No. 1.0074 0.9927 0.9681 1.0482
## DRB345.Mismatch.No. 0.9575 1.0444 0.9268 0.9892
## DP.Mismatch.No. 0.9819 1.0184 0.9532 1.0115
## DQ.Mismatch.No. 1.0317 0.9693 0.9956 1.0691
## age 0.9687 1.0323 0.9495 0.9884
##
## Concordance= 0.727 (se = 0.035 )
## Likelihood ratio test= 24.79 on 8 df, p=0.002
## Wald test = 25.71 on 8 df, p=0.001
## Score (logrank) test = 26.9 on 8 df, p=7e-04Grade 1 coxph antigen
cox_results<-univariate_coxph(df, independent_al_vars, "Status.G1", "Med.Time.G1")## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside##
## Cox proportional hazards model for A :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## A 0.1665 1.1811 0.1899 0.876 0.381
##
## exp(coef) exp(-coef) lower .95 upper .95
## A 1.181 0.8467 0.814 1.714
##
## Concordance= 0.516 (se = 0.039 )
## Likelihood ratio test= 0.79 on 1 df, p=0.4
## Wald test = 0.77 on 1 df, p=0.4
## Score (logrank) test = 0.77 on 1 df, p=0.4## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for B :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## B 0.06843 1.07083 0.25734 0.266 0.79
##
## exp(coef) exp(-coef) lower .95 upper .95
## B 1.071 0.9339 0.6466 1.773
##
## Concordance= 0.521 (se = 0.031 )
## Likelihood ratio test= 0.07 on 1 df, p=0.8
## Wald test = 0.07 on 1 df, p=0.8
## Score (logrank) test = 0.07 on 1 df, p=0.8## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for C :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## C -0.1132 0.8930 0.1589 -0.713 0.476
##
## exp(coef) exp(-coef) lower .95 upper .95
## C 0.893 1.12 0.6541 1.219
##
## Concordance= 0.528 (se = 0.041 )
## Likelihood ratio test= 0.5 on 1 df, p=0.5
## Wald test = 0.51 on 1 df, p=0.5
## Score (logrank) test = 0.51 on 1 df, p=0.5## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for DRB1 :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB1 -0.00464 0.99537 0.17558 -0.026 0.979
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB1 0.9954 1.005 0.7056 1.404
##
## Concordance= 0.483 (se = 0.038 )
## Likelihood ratio test= 0 on 1 df, p=1
## Wald test = 0 on 1 df, p=1
## Score (logrank) test = 0 on 1 df, p=1## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for DRB345 :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB345 -0.05932 0.94241 0.05821 -1.019 0.308
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB345 0.9424 1.061 0.8408 1.056
##
## Concordance= 0.518 (se = 0.042 )
## Likelihood ratio test= 1.05 on 1 df, p=0.3
## Wald test = 1.04 on 1 df, p=0.3
## Score (logrank) test = 1.04 on 1 df, p=0.3## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for DP :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DP -0.15745 0.85432 0.09407 -1.674 0.0942 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## DP 0.8543 1.171 0.7105 1.027
##
## Concordance= 0.582 (se = 0.045 )
## Likelihood ratio test= 2.78 on 1 df, p=0.1
## Wald test = 2.8 on 1 df, p=0.09
## Score (logrank) test = 2.81 on 1 df, p=0.09## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for DQ :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DQ 0.04681 1.04793 0.09354 0.5 0.617
##
## exp(coef) exp(-coef) lower .95 upper .95
## DQ 1.048 0.9543 0.8724 1.259
##
## Concordance= 0.547 (se = 0.045 )
## Likelihood ratio test= 0.25 on 1 df, p=0.6
## Wald test = 0.25 on 1 df, p=0.6
## Score (logrank) test = 0.25 on 1 df, p=0.6## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
##
## Cox proportional hazards model for age :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## age -0.03635 0.96430 0.00961 -3.783 0.000155 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## age 0.9643 1.037 0.9463 0.9826
##
## Concordance= 0.638 (se = 0.038 )
## Likelihood ratio test= 12.55 on 1 df, p=4e-04
## Wald test = 14.31 on 1 df, p=2e-04
## Score (logrank) test = 14.54 on 1 df, p=1e-04
##
## Variable HR Lower_CI Upper_CI p_value C_index
## A A 1.1811174 0.8140199 1.713764 0.3807632086 0.5157551
## B B 1.0708253 0.6466464 1.773252 0.7903107441 0.5212545
## C C 0.8929849 0.6540734 1.219163 0.4761499600 0.5280916
## DRB1 DRB1 0.9953712 0.7055544 1.404234 0.9789192987 0.4829073
## DRB345 DRB345 0.9424080 0.8407948 1.056302 0.3081997742 0.5184304
## DP DP 0.8543197 0.7104707 1.027294 0.0941863894 0.5820452
## DQ DQ 1.0479271 0.8723862 1.258790 0.6167478034 0.5466706
## age age 0.9643023 0.9463096 0.982637 0.0001551797 0.6382283cox_multi<-coxph(Surv(Med.Time.G1, Status.G1) ~ A + B + C + DRB1 + DRB345 + DP + DQ +age, data=df)
summary(cox_multi)## Call:
## coxph(formula = Surv(Med.Time.G1, Status.G1) ~ A + B + C + DRB1 +
## DRB345 + DP + DQ + age, data = df)
##
## n= 98, number of events= 96
##
## coef exp(coef) se(coef) z Pr(>|z|)
## A 0.15483 1.16746 0.20462 0.757 0.44926
## B 0.15349 1.16590 0.28542 0.538 0.59073
## C -0.12841 0.87949 0.17697 -0.726 0.46808
## DRB1 0.01155 1.01162 0.23437 0.049 0.96068
## DRB345 -0.06878 0.93353 0.06894 -0.998 0.31846
## DP -0.19582 0.82216 0.09935 -1.971 0.04872 *
## DQ 0.08192 1.08536 0.11669 0.702 0.48268
## age -0.03373 0.96683 0.01001 -3.371 0.00075 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## A 1.1675 0.8566 0.7817 1.7435
## B 1.1659 0.8577 0.6664 2.0399
## C 0.8795 1.1370 0.6217 1.2441
## DRB1 1.0116 0.9885 0.6390 1.6014
## DRB345 0.9335 1.0712 0.8155 1.0686
## DP 0.8222 1.2163 0.6767 0.9989
## DQ 1.0854 0.9213 0.8635 1.3643
## age 0.9668 1.0343 0.9481 0.9860
##
## Concordance= 0.696 (se = 0.037 )
## Likelihood ratio test= 18.34 on 8 df, p=0.02
## Wald test = 20.05 on 8 df, p=0.01
## Score (logrank) test = 20.55 on 8 df, p=0.008ggcoxzph(cox.zph(cox_multi))## Warning in splines::ns(temp, df = df, intercept = TRUE): shoving 'interior'
## knots matching boundary knots to inside
Grade 2 coxph eplet
cox_results<-univariate_coxph(df, independent_vars, "Status.G2", "Med.Time.G2")
##
## Cox proportional hazards model for A.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## A.Mismatch.No. 0.03169 1.03220 0.02436 1.301 0.193
##
## exp(coef) exp(-coef) lower .95 upper .95
## A.Mismatch.No. 1.032 0.9688 0.9841 1.083
##
## Concordance= 0.581 (se = 0.051 )
## Likelihood ratio test= 1.69 on 1 df, p=0.2
## Wald test = 1.69 on 1 df, p=0.2
## Score (logrank) test = 1.69 on 1 df, p=0.2
##
## Cox proportional hazards model for B.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## B.Mismatch.No. 0.04873 1.04994 0.02911 1.674 0.0941 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## B.Mismatch.No. 1.05 0.9524 0.9917 1.112
##
## Concordance= 0.581 (se = 0.053 )
## Likelihood ratio test= 2.8 on 1 df, p=0.09
## Wald test = 2.8 on 1 df, p=0.09
## Score (logrank) test = 2.82 on 1 df, p=0.09
##
## Cox proportional hazards model for C.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## C.Mismatch.No. 0.006271 1.006290 0.033584 0.187 0.852
##
## exp(coef) exp(-coef) lower .95 upper .95
## C.Mismatch.No. 1.006 0.9937 0.9422 1.075
##
## Concordance= 0.508 (se = 0.051 )
## Likelihood ratio test= 0.03 on 1 df, p=0.9
## Wald test = 0.03 on 1 df, p=0.9
## Score (logrank) test = 0.03 on 1 df, p=0.9
##
## Cox proportional hazards model for DRB1.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB1.Mismatch.No. 0.04261 1.04353 0.02795 1.525 0.127
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB1.Mismatch.No. 1.044 0.9583 0.9879 1.102
##
## Concordance= 0.577 (se = 0.054 )
## Likelihood ratio test= 2.32 on 1 df, p=0.1
## Wald test = 2.33 on 1 df, p=0.1
## Score (logrank) test = 2.34 on 1 df, p=0.1
##
## Cox proportional hazards model for DRB345.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB345.Mismatch.No. 0.006783 1.006806 0.020400 0.333 0.739
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB345.Mismatch.No. 1.007 0.9932 0.9673 1.048
##
## Concordance= 0.537 (se = 0.056 )
## Likelihood ratio test= 0.11 on 1 df, p=0.7
## Wald test = 0.11 on 1 df, p=0.7
## Score (logrank) test = 0.11 on 1 df, p=0.7
##
## Cox proportional hazards model for DP.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DP.Mismatch.No. -0.03786 0.96285 0.02566 -1.475 0.14
##
## exp(coef) exp(-coef) lower .95 upper .95
## DP.Mismatch.No. 0.9628 1.039 0.9156 1.013
##
## Concordance= 0.584 (se = 0.054 )
## Likelihood ratio test= 2.26 on 1 df, p=0.1
## Wald test = 2.18 on 1 df, p=0.1
## Score (logrank) test = 2.2 on 1 df, p=0.1
##
## Cox proportional hazards model for DQ.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DQ.Mismatch.No. 0.06890 1.07133 0.02399 2.872 0.00408 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## DQ.Mismatch.No. 1.071 0.9334 1.022 1.123
##
## Concordance= 0.661 (se = 0.046 )
## Likelihood ratio test= 7.87 on 1 df, p=0.005
## Wald test = 8.25 on 1 df, p=0.004
## Score (logrank) test = 8.36 on 1 df, p=0.004
##
## Cox proportional hazards model for age :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## age -0.04138 0.95946 0.01317 -3.141 0.00168 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## age 0.9595 1.042 0.935 0.9846
##
## Concordance= 0.631 (se = 0.054 )
## Likelihood ratio test= 8.29 on 1 df, p=0.004
## Wald test = 9.87 on 1 df, p=0.002
## Score (logrank) test = 10.14 on 1 df, p=0.001
##
## Variable HR Lower_CI Upper_CI
## A.Mismatch.No. A.Mismatch.No. 1.0322016 0.9840738 1.0826832
## B.Mismatch.No. B.Mismatch.No. 1.0499368 0.9917195 1.1115716
## C.Mismatch.No. C.Mismatch.No. 1.0062902 0.9421854 1.0747567
## DRB1.Mismatch.No. DRB1.Mismatch.No. 1.0435337 0.9879151 1.1022835
## DRB345.Mismatch.No. DRB345.Mismatch.No. 1.0068064 0.9673459 1.0478767
## DP.Mismatch.No. DP.Mismatch.No. 0.9628452 0.9156111 1.0125160
## DQ.Mismatch.No. DQ.Mismatch.No. 1.0713328 1.0221187 1.1229166
## age age 0.9594634 0.9350077 0.9845588
## p_value C_index
## A.Mismatch.No. 0.193270122 0.5811124
## B.Mismatch.No. 0.094076202 0.5813055
## C.Mismatch.No. 0.851887464 0.5075319
## DRB1.Mismatch.No. 0.127289682 0.5770568
## DRB345.Mismatch.No. 0.739492864 0.5365006
## DP.Mismatch.No. 0.140129484 0.5836230
## DQ.Mismatch.No. 0.004081578 0.6612592
## age 0.001682256 0.6307455cox_multi<-coxph(Surv(Med.Time.G2, Status.G2) ~ A.Mismatch.No. + B.Mismatch.No. + C.Mismatch.No. + DRB1.Mismatch.No. + DRB345.Mismatch.No. + DP.Mismatch.No. + DQ.Mismatch.No. +age, data=df)
ggcoxzph(cox.zph(cox_multi))
summary(cox_multi)## Call:
## coxph(formula = Surv(Med.Time.G2, Status.G2) ~ A.Mismatch.No. +
## B.Mismatch.No. + C.Mismatch.No. + DRB1.Mismatch.No. + DRB345.Mismatch.No. +
## DP.Mismatch.No. + DQ.Mismatch.No. + age, data = df)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## A.Mismatch.No. 0.008364 1.008400 0.027652 0.302 0.762276
## B.Mismatch.No. 0.024613 1.024918 0.032166 0.765 0.444161
## C.Mismatch.No. 0.003002 1.003006 0.039270 0.076 0.939075
## DRB1.Mismatch.No. 0.047584 1.048734 0.036248 1.313 0.189275
## DRB345.Mismatch.No. -0.012720 0.987360 0.023776 -0.535 0.592641
## DP.Mismatch.No. -0.062483 0.939429 0.025554 -2.445 0.014478 *
## DQ.Mismatch.No. 0.058841 1.060606 0.026983 2.181 0.029212 *
## age -0.054905 0.946575 0.015141 -3.626 0.000287 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## A.Mismatch.No. 1.0084 0.9917 0.9552 1.0646
## B.Mismatch.No. 1.0249 0.9757 0.9623 1.0916
## C.Mismatch.No. 1.0030 0.9970 0.9287 1.0833
## DRB1.Mismatch.No. 1.0487 0.9535 0.9768 1.1260
## DRB345.Mismatch.No. 0.9874 1.0128 0.9424 1.0345
## DP.Mismatch.No. 0.9394 1.0645 0.8935 0.9877
## DQ.Mismatch.No. 1.0606 0.9429 1.0060 1.1182
## age 0.9466 1.0564 0.9189 0.9751
##
## Concordance= 0.744 (se = 0.046 )
## Likelihood ratio test= 24.79 on 8 df, p=0.002
## Wald test = 24.82 on 8 df, p=0.002
## Score (logrank) test = 25.7 on 8 df, p=0.001Grade 2 coxph antigen
cox_results<-univariate_coxph(df, independent_al_vars, "Status.G2", "Med.Time.G2")
##
## Cox proportional hazards model for A :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## A 0.09735 1.10225 0.34331 0.284 0.777
##
## exp(coef) exp(-coef) lower .95 upper .95
## A 1.102 0.9072 0.5624 2.16
##
## Concordance= 0.506 (se = 0.044 )
## Likelihood ratio test= 0.08 on 1 df, p=0.8
## Wald test = 0.08 on 1 df, p=0.8
## Score (logrank) test = 0.08 on 1 df, p=0.8
##
## Cox proportional hazards model for B :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## B 1.096 2.993 0.605 1.812 0.07 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## B 2.992 0.3342 0.9143 9.795
##
## Concordance= 0.575 (se = 0.029 )
## Likelihood ratio test= 4.43 on 1 df, p=0.04
## Wald test = 3.28 on 1 df, p=0.07
## Score (logrank) test = 3.62 on 1 df, p=0.06
##
## Cox proportional hazards model for C :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## C 0.08003 1.08332 0.28292 0.283 0.777
##
## exp(coef) exp(-coef) lower .95 upper .95
## C 1.083 0.9231 0.6222 1.886
##
## Concordance= 0.514 (se = 0.045 )
## Likelihood ratio test= 0.08 on 1 df, p=0.8
## Wald test = 0.08 on 1 df, p=0.8
## Score (logrank) test = 0.08 on 1 df, p=0.8
##
## Cox proportional hazards model for DRB1 :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB1 0.3188 1.3754 0.3196 0.997 0.319
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB1 1.375 0.727 0.7351 2.573
##
## Concordance= 0.546 (se = 0.043 )
## Likelihood ratio test= 1.06 on 1 df, p=0.3
## Wald test = 0.99 on 1 df, p=0.3
## Score (logrank) test = 1 on 1 df, p=0.3
##
## Cox proportional hazards model for DRB345 :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB345 -0.005122 0.994891 0.093669 -0.055 0.956
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB345 0.9949 1.005 0.828 1.195
##
## Concordance= 0.491 (se = 0.05 )
## Likelihood ratio test= 0 on 1 df, p=1
## Wald test = 0 on 1 df, p=1
## Score (logrank) test = 0 on 1 df, p=1
##
## Cox proportional hazards model for DP :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DP -0.4018 0.6691 0.1568 -2.562 0.0104 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## DP 0.6691 1.494 0.4921 0.9099
##
## Concordance= 0.63 (se = 0.053 )
## Likelihood ratio test= 6.48 on 1 df, p=0.01
## Wald test = 6.57 on 1 df, p=0.01
## Score (logrank) test = 6.72 on 1 df, p=0.01
##
## Cox proportional hazards model for DQ :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DQ 0.4520 1.5715 0.1809 2.499 0.0124 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## DQ 1.571 0.6363 1.102 2.24
##
## Concordance= 0.624 (se = 0.049 )
## Likelihood ratio test= 6.84 on 1 df, p=0.009
## Wald test = 6.25 on 1 df, p=0.01
## Score (logrank) test = 6.41 on 1 df, p=0.01
##
## Cox proportional hazards model for age :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 98, number of events= 34
##
## coef exp(coef) se(coef) z Pr(>|z|)
## age -0.04138 0.95946 0.01317 -3.141 0.00168 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## age 0.9595 1.042 0.935 0.9846
##
## Concordance= 0.631 (se = 0.054 )
## Likelihood ratio test= 8.29 on 1 df, p=0.004
## Wald test = 9.87 on 1 df, p=0.002
## Score (logrank) test = 10.14 on 1 df, p=0.001
##
## Variable HR Lower_CI Upper_CI p_value C_index
## A A 1.1022508 0.5624173 2.1602409 0.776732635 0.5057937
## B B 2.9924740 0.9142523 9.7947807 0.070020100 0.5749324
## C C 1.0833205 0.6222100 1.8861531 0.777271200 0.5142912
## DRB1 DRB1 1.3754244 0.7351120 2.5734750 0.318650296 0.5459637
## DRB345 DRB345 0.9948913 0.8280269 1.1953822 0.956393252 0.4911163
## DP DP 0.6691286 0.4920872 0.9098652 0.010395637 0.6295867
## DQ DQ 1.5714794 1.1024534 2.2400471 0.012445126 0.6235998
## age age 0.9594634 0.9350077 0.9845588 0.001682256 0.6307455data pivoting longer
time_cols<-c("X1", "X2", "X3", "X4", "X6", "X8", "X10", "X12", "X26", "X52")
df_long<-df %>% pivot_longer(cols = time_cols, names_to = "Time_biopsy", values_to = "Grade")## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(time_cols)
##
## # Now:
## data %>% select(all_of(time_cols))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.df_long$Time_biopsy<-df_long$Time_biopsy %>% gsub("X", "", .) %>% as.numeric()
df_long$Grade[df_long$Grade==9]<-NA
df_long$Grade<-df_long$Grade+1
df_long$Grade<-df_long$Grade %>% as.factor()
df_long <- df_long[order(df_long$Time_biopsy),]
df_long<-df_long %>% filter(!is.na(Grade))function to write repeated measures PO model
create_pomtim_formula <- function(response_var, independent_vars, time,df) {
all_vars <- c(response_var, independent_vars)
missing_vars <- setdiff(all_vars, names(df))
if(length(missing_vars) > 0) { stop(paste("The following variables are missing in the dataframe:",
paste(missing_vars, collapse=", "), "."))
}
formula_str <- paste(response_var, "~", paste(independent_vars, collapse = " + "), "+", time)
return(as.formula(formula_str))
}
create_pom_formula <- function(response_var, independent_vars, time,df) {
all_vars <- c(response_var, independent_vars)
missing_vars <- setdiff(all_vars, names(df))
if(length(missing_vars) > 0) { stop(paste("The following variables are missing in the dataframe:",
paste(missing_vars, collapse=", "), "."))
}
formula_str <- paste(response_var, "~", paste(independent_vars, collapse = " + "))
return(as.formula(formula_str))
}Repeated measures PO model
independent_vars## [1] "A.Mismatch.No." "B.Mismatch.No." "C.Mismatch.No."
## [4] "DRB1.Mismatch.No." "DRB345.Mismatch.No." "DP.Mismatch.No."
## [7] "DQ.Mismatch.No." "age"pom_formula <- create_pomtim_formula("Grade", independent_vars, "Time_biopsy" ,df_long)
pom_formula## Grade ~ A.Mismatch.No. + B.Mismatch.No. + C.Mismatch.No. + DRB1.Mismatch.No. +
## DRB345.Mismatch.No. + DP.Mismatch.No. + DQ.Mismatch.No. +
## age + Time_biopsy
## <environment: 0x5f649afbaa80>model <- polr(pom_formula, data = df_long)
#model <- polr(Grade ~ Time_biopsy, data = df_long, Hess = TRUE)
coefficients <- summary(model)$coefficients##
## Re-fitting to get Hessianp_value <- (1 - pnorm(abs(coefficients[ ,"t value"]), 0, 1))*2
coefficients <- summary(model)$coefficients##
## Re-fitting to get Hessiancoefficients <- cbind(coefficients, p_value)
# calculate odds ratios
coefficients <- cbind(coefficients, odds_ratio = exp(coefficients[ ,"Value"]))
# combine with coefficient and p_value
printCoefmat(coefficients[ ,c("Value", "Std. Error", "odds_ratio", "p_value")],
P.values=TRUE,
has.Pvalue=TRUE)## Value Std. Error odds_ratio p_value
## A.Mismatch.No. 0.00050392 0.01055601 1.0005 0.961925
## B.Mismatch.No. 0.01370436 0.01208611 1.0138 0.256839
## C.Mismatch.No. -0.02600325 0.01449154 0.9743 0.072753 .
## DRB1.Mismatch.No. 0.03668191 0.01308992 1.0374 0.005074 **
## DRB345.Mismatch.No. -0.03438792 0.00964633 0.9662 0.000364 ***
## DP.Mismatch.No. -0.02142974 0.00948431 0.9788 0.023853 *
## DQ.Mismatch.No. 0.03782805 0.01135264 1.0386 0.000862 ***
## age -0.01709165 0.00609452 0.9831 0.005041 **
## Time_biopsy -0.00219586 0.00458371 0.9978 0.631898
## 1|2 -0.95908914 0.42037203 0.3832 0.022517 *
## 2|3 2.34344035 0.43440975 10.4170 6.870e-08 ***
## 3|4 6.30458471 1.08049763 547.0743 5.383e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(brant)
brant(model)## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## ----------------------------------------------------
## Test for X2 df probability
## ----------------------------------------------------
## Omnibus 8.3 18 0.97
## A.Mismatch.No. 0 2 1
## B.Mismatch.No. 3.06 2 0.22
## C.Mismatch.No. 0 2 1
## DRB1.Mismatch.No. -0.12 2 1
## DRB345.Mismatch.No. 0.7 2 0.71
## DP.Mismatch.No. -0.01 2 1
## DQ.Mismatch.No. 0.06 2 0.97
## age 0 2 1
## Time_biopsy 0 2 1
## ----------------------------------------------------
##
## H0: Parallel Regression Assumption holdsdf_long$trans_id<-df_long%>%filter()
glm(pom_formula, data = df_long, family = binomial(link = "logit"))##
## Call: glm(formula = pom_formula, family = binomial(link = "logit"),
## data = df_long)
##
## Coefficients:
## (Intercept) A.Mismatch.No. B.Mismatch.No.
## 0.895554 -0.001885 0.009735
## C.Mismatch.No. DRB1.Mismatch.No. DRB345.Mismatch.No.
## -0.028640 0.036148 -0.036378
## DP.Mismatch.No. DQ.Mismatch.No. age
## -0.016782 0.039024 -0.015641
## Time_biopsy
## 0.001610
##
## Degrees of Freedom: 897 Total (i.e. Null); 888 Residual
## Null Deviance: 1219
## Residual Deviance: 1178 AIC: 1198#repolr(pom_formula, subjects = "trans_id",data = df_long, categories = 4, times = c(1, 2, 3, 4, 6, 8, 10, 12, 26, 52), corstr = "ar1",
# tol = 0.001, scalevalue = 1, alpha = 0.5, po.test=TRUE, fixed=FALSE) po with max grades
df$Max_Grade<-df$Max.Grade %>% as.factor()
pom_formula2 <- create_pom_formula("Max_Grade", independent_vars, "time2fail" ,df)
model2 <- polr(pom_formula2, data = df, Hess = TRUE)
coefficients <- summary(model2)$coefficients
p_value <- (1 - pnorm(abs(coefficients[ ,"t value"]), 0, 1))*2
coefficients <- cbind(coefficients, p_value)
coefficients <- cbind(coefficients, odds_ratio = exp(coefficients[ ,"Value"]))
printCoefmat(coefficients[ ,c("Value", "Std. Error", "odds_ratio", "p_value")],
P.values=TRUE,
has.Pvalue=TRUE)## Value Std. Error odds_ratio p_value
## A.Mismatch.No. 0.0180628 0.0362504 1.0182 0.6182876
## B.Mismatch.No. 0.0313153 0.0399608 1.0318 0.4332454
## C.Mismatch.No. -0.0084999 0.0498731 0.9915 0.8646724
## DRB1.Mismatch.No. 0.0444439 0.0456208 1.0454 0.3299557
## DRB345.Mismatch.No. -0.0282089 0.0319086 0.9722 0.3766672
## DP.Mismatch.No. -0.0713978 0.0334554 0.9311 0.0328330 *
## DQ.Mismatch.No. 0.0840075 0.0383948 1.0876 0.0286700 *
## age -0.0567930 0.0216237 0.9448 0.0086286 **
## 0|1 -6.2233976 1.6339131 0.0020 0.0001396 ***
## 1|2 -1.0775802 1.4072995 0.3404 0.4438502
## 2|3 3.3304985 1.6404126 27.9523 0.0423280 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1conf<-exp(confint(model2, level = 0.90))## Waiting for profiling to be done...var<-c("A.Mismatch.No.", "B.Mismatch.No.", "C.Mismatch.No.", "", "DP.Mismatch.No.", "DQ.Mismatch.No.", "age")
lower<-c(conf[,1])
upper<-c(conf[,2])
odds<-exp(coefficients[ 0:8,"Value"])
ndf <- data.frame(Variable = independent_vars, Lower = lower, Upper = upper, Odds = odds)
ndf$err <- ndf$Upper - ndf$Odds
svg("figs/forest_plot.svg")
ndf %>% forestplot(labeltext=Variable, mean=Odds, lower=Lower, upper=Upper, xlab="Odds Ratio", zero = 1,
cex = 2, lineheight = "auto")
dev.off()## png
## 2df$X1st.DSA<-anydate(df$X1st.DSA)
df$X1st.DSA_status<-ifelse(df$X1st.DSA==anydate("2024-03-10"), 0, 1)
df$X1st.DSA<-ifelse(is.na(df$X1st.DSA), anydate("2024-03-10"), df$X1st.DSA)
df$X1st.DSA_time<-anydate(df$X1st.DSA)-df$Transplant.Date
df$X1st.DSA_status## [1] 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1 1 NA 1 NA NA NA NA NA NA
## [26] NA 1 NA NA NA NA NA NA NA NA NA NA NA NA NA 1 NA 1 NA 1 NA 1 NA NA NA
## [51] NA NA NA NA 1 NA NA NA NA 1 NA NA 1 NA NA 1 NA NA NA NA NA NA NA NA NA
## [76] 1 NA NA NA NA 1 NA 1 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NAdf$Transplant.Date## [1] "2023-06-23" "2023-03-22" "2023-02-20" "2022-10-16" "2022-07-24"
## [6] "2022-04-10" "2022-03-20" "2022-02-02" "2022-01-09" "2022-01-07"
## [11] "2021-11-07" "2021-09-09" "2021-09-02" "2021-07-16" "2021-06-07"
## [16] "2021-04-19" "2020-11-16" "2020-11-07" "2020-09-15" "2020-08-20"
## [21] "2020-07-04" "2020-06-27" "2020-04-13" "2020-03-11" "2020-01-16"
## [26] "2020-01-02" "2019-11-04" "2019-10-24" "2019-09-30" "2019-09-28"
## [31] "2019-09-11" "2019-09-01" "2019-08-14" "2019-07-29" "2019-06-20"
## [36] "2019-06-16" "2019-04-07" "2019-02-16" "2019-01-05" "2018-12-19"
## [41] "2018-12-11" "2018-12-04" "2018-07-19" "2018-06-29" "2018-06-28"
## [46] "2018-06-28" "2018-06-22" "2018-04-04" "2018-02-09" "2018-02-04"
## [51] "2017-11-08" "2017-10-29" "2017-10-29" "2017-10-09" "2017-09-15"
## [56] "2017-08-15" "2017-05-24" "2017-05-09" "2017-03-14" "2017-02-26"
## [61] "2017-02-22" "2017-02-18" "2016-11-06" "2016-10-17" "2016-09-12"
## [66] "2016-07-31" "2016-05-15" "2016-05-07" "2016-04-26" "2016-03-27"
## [71] "2015-12-11" "2015-09-25" "2015-09-14" "2015-06-26" "2015-04-02"
## [76] "2015-03-12" "2015-02-02" "2015-01-31" "2014-09-30" "2014-09-15"
## [81] "2014-08-15" "2014-08-02" "2014-06-19" "2014-03-26" "2013-11-29"
## [86] "2013-10-22" "2013-08-08" "2013-07-07" "2013-02-23" "2012-07-14"
## [91] "2012-06-30" "2011-10-21" "2011-10-18" "2011-08-19" "2011-07-01"
## [96] "2011-06-23" "2011-02-10" "2010-11-15"ggsurvplot(surv_fit(Surv(X1st.DSA_time, X1st.DSA_status) ~ 1, data = df),
data = df,
xlab = "Time to Failure",
ylab = "Survival Probability",
title = "Kaplan-Meier Survival Curves",)
univariate_coxph(df, independent_vars, "X1st.DSA_status", "X1st.DSA_time")
##
## Cox proportional hazards model for A.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## A.Mismatch.No. 0.01999 1.02019 0.03804 0.525 0.599
##
## exp(coef) exp(-coef) lower .95 upper .95
## A.Mismatch.No. 1.02 0.9802 0.9469 1.099
##
## Concordance= 0.5 (se = 0.077 )
## Likelihood ratio test= 0.27 on 1 df, p=0.6
## Wald test = 0.28 on 1 df, p=0.6
## Score (logrank) test = 0.28 on 1 df, p=0.6
##
## Cox proportional hazards model for B.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## B.Mismatch.No. 0.04676 1.04787 0.04196 1.114 0.265
##
## exp(coef) exp(-coef) lower .95 upper .95
## B.Mismatch.No. 1.048 0.9543 0.9651 1.138
##
## Concordance= 0.665 (se = 0.071 )
## Likelihood ratio test= 1.26 on 1 df, p=0.3
## Wald test = 1.24 on 1 df, p=0.3
## Score (logrank) test = 1.25 on 1 df, p=0.3
##
## Cox proportional hazards model for C.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## C.Mismatch.No. -0.03032 0.97014 0.05255 -0.577 0.564
##
## exp(coef) exp(-coef) lower .95 upper .95
## C.Mismatch.No. 0.9701 1.031 0.8752 1.075
##
## Concordance= 0.515 (se = 0.071 )
## Likelihood ratio test= 0.33 on 1 df, p=0.6
## Wald test = 0.33 on 1 df, p=0.6
## Score (logrank) test = 0.33 on 1 df, p=0.6
##
## Cox proportional hazards model for DRB1.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB1.Mismatch.No. 0.02296 1.02323 0.04633 0.496 0.62
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB1.Mismatch.No. 1.023 0.9773 0.9344 1.12
##
## Concordance= 0.548 (se = 0.111 )
## Likelihood ratio test= 0.25 on 1 df, p=0.6
## Wald test = 0.25 on 1 df, p=0.6
## Score (logrank) test = 0.25 on 1 df, p=0.6
##
## Cox proportional hazards model for DRB345.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB345.Mismatch.No. 0.01726 1.01741 0.03941 0.438 0.661
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB345.Mismatch.No. 1.017 0.9829 0.9418 1.099
##
## Concordance= 0.529 (se = 0.083 )
## Likelihood ratio test= 0.19 on 1 df, p=0.7
## Wald test = 0.19 on 1 df, p=0.7
## Score (logrank) test = 0.19 on 1 df, p=0.7
##
## Cox proportional hazards model for DP.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DP.Mismatch.No. -0.01717 0.98298 0.03961 -0.434 0.665
##
## exp(coef) exp(-coef) lower .95 upper .95
## DP.Mismatch.No. 0.983 1.017 0.9096 1.062
##
## Concordance= 0.485 (se = 0.082 )
## Likelihood ratio test= 0.19 on 1 df, p=0.7
## Wald test = 0.19 on 1 df, p=0.7
## Score (logrank) test = 0.19 on 1 df, p=0.7
##
## Cox proportional hazards model for DQ.Mismatch.No. :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DQ.Mismatch.No. -0.02360 0.97668 0.03926 -0.601 0.548
##
## exp(coef) exp(-coef) lower .95 upper .95
## DQ.Mismatch.No. 0.9767 1.024 0.9044 1.055
##
## Concordance= 0.562 (se = 0.097 )
## Likelihood ratio test= 0.36 on 1 df, p=0.5
## Wald test = 0.36 on 1 df, p=0.5
## Score (logrank) test = 0.36 on 1 df, p=0.5
##
## Cox proportional hazards model for age :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## age -0.01713 0.98302 0.02255 -0.76 0.448
##
## exp(coef) exp(-coef) lower .95 upper .95
## age 0.983 1.017 0.9405 1.027
##
## Concordance= 0.632 (se = 0.088 )
## Likelihood ratio test= 0.52 on 1 df, p=0.5
## Wald test = 0.58 on 1 df, p=0.4
## Score (logrank) test = 0.58 on 1 df, p=0.4
##
## Variable HR Lower_CI Upper_CI p_value
## A.Mismatch.No. A.Mismatch.No. 1.0201888 0.9468937 1.099157 0.5992739
## B.Mismatch.No. B.Mismatch.No. 1.0478670 0.9651414 1.137683 0.2651276
## C.Mismatch.No. C.Mismatch.No. 0.9701363 0.8751862 1.075388 0.5639887
## DRB1.Mismatch.No. DRB1.Mismatch.No. 1.0232300 0.9344066 1.120497 0.6201388
## DRB345.Mismatch.No. DRB345.Mismatch.No. 1.0174110 0.9417806 1.099115 0.6614020
## DP.Mismatch.No. DP.Mismatch.No. 0.9829767 0.9095570 1.062323 0.6646446
## DQ.Mismatch.No. DQ.Mismatch.No. 0.9766793 0.9043500 1.054793 0.5477780
## age age 0.9830200 0.9405224 1.027438 0.4475451
## C_index
## A.Mismatch.No. 0.5000000
## B.Mismatch.No. 0.6654412
## C.Mismatch.No. 0.5147059
## DRB1.Mismatch.No. 0.5477941
## DRB345.Mismatch.No. 0.5294118
## DP.Mismatch.No. 0.4852941
## DQ.Mismatch.No. 0.5625000
## age 0.6323529## $A.Mismatch.No.
## $A.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## A.Mismatch.No. 0.01999 1.02019 0.03804 0.525 0.599
##
## Likelihood ratio test=0.27 on 1 df, p=0.6012
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## $A.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## A.Mismatch.No. 0.01999 1.02019 0.03804 0.525 0.599
##
## exp(coef) exp(-coef) lower .95 upper .95
## A.Mismatch.No. 1.02 0.9802 0.9469 1.099
##
## Concordance= 0.5 (se = 0.077 )
## Likelihood ratio test= 0.27 on 1 df, p=0.6
## Wald test = 0.28 on 1 df, p=0.6
## Score (logrank) test = 0.28 on 1 df, p=0.6
##
##
##
## $B.Mismatch.No.
## $B.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## B.Mismatch.No. 0.04676 1.04787 0.04196 1.114 0.265
##
## Likelihood ratio test=1.26 on 1 df, p=0.2607
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## $B.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## B.Mismatch.No. 0.04676 1.04787 0.04196 1.114 0.265
##
## exp(coef) exp(-coef) lower .95 upper .95
## B.Mismatch.No. 1.048 0.9543 0.9651 1.138
##
## Concordance= 0.665 (se = 0.071 )
## Likelihood ratio test= 1.26 on 1 df, p=0.3
## Wald test = 1.24 on 1 df, p=0.3
## Score (logrank) test = 1.25 on 1 df, p=0.3
##
##
##
## $C.Mismatch.No.
## $C.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## C.Mismatch.No. -0.03032 0.97014 0.05255 -0.577 0.564
##
## Likelihood ratio test=0.33 on 1 df, p=0.5642
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## $C.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## C.Mismatch.No. -0.03032 0.97014 0.05255 -0.577 0.564
##
## exp(coef) exp(-coef) lower .95 upper .95
## C.Mismatch.No. 0.9701 1.031 0.8752 1.075
##
## Concordance= 0.515 (se = 0.071 )
## Likelihood ratio test= 0.33 on 1 df, p=0.6
## Wald test = 0.33 on 1 df, p=0.6
## Score (logrank) test = 0.33 on 1 df, p=0.6
##
##
##
## $DRB1.Mismatch.No.
## $DRB1.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## DRB1.Mismatch.No. 0.02296 1.02323 0.04633 0.496 0.62
##
## Likelihood ratio test=0.25 on 1 df, p=0.6156
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## $DRB1.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB1.Mismatch.No. 0.02296 1.02323 0.04633 0.496 0.62
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB1.Mismatch.No. 1.023 0.9773 0.9344 1.12
##
## Concordance= 0.548 (se = 0.111 )
## Likelihood ratio test= 0.25 on 1 df, p=0.6
## Wald test = 0.25 on 1 df, p=0.6
## Score (logrank) test = 0.25 on 1 df, p=0.6
##
##
##
## $DRB345.Mismatch.No.
## $DRB345.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## DRB345.Mismatch.No. 0.01726 1.01741 0.03941 0.438 0.661
##
## Likelihood ratio test=0.19 on 1 df, p=0.6607
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## $DRB345.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB345.Mismatch.No. 0.01726 1.01741 0.03941 0.438 0.661
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB345.Mismatch.No. 1.017 0.9829 0.9418 1.099
##
## Concordance= 0.529 (se = 0.083 )
## Likelihood ratio test= 0.19 on 1 df, p=0.7
## Wald test = 0.19 on 1 df, p=0.7
## Score (logrank) test = 0.19 on 1 df, p=0.7
##
##
##
## $DP.Mismatch.No.
## $DP.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## DP.Mismatch.No. -0.01717 0.98298 0.03961 -0.434 0.665
##
## Likelihood ratio test=0.19 on 1 df, p=0.6648
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## $DP.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DP.Mismatch.No. -0.01717 0.98298 0.03961 -0.434 0.665
##
## exp(coef) exp(-coef) lower .95 upper .95
## DP.Mismatch.No. 0.983 1.017 0.9096 1.062
##
## Concordance= 0.485 (se = 0.082 )
## Likelihood ratio test= 0.19 on 1 df, p=0.7
## Wald test = 0.19 on 1 df, p=0.7
## Score (logrank) test = 0.19 on 1 df, p=0.7
##
##
##
## $DQ.Mismatch.No.
## $DQ.Mismatch.No.$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## DQ.Mismatch.No. -0.02360 0.97668 0.03926 -0.601 0.548
##
## Likelihood ratio test=0.36 on 1 df, p=0.5479
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## $DQ.Mismatch.No.$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DQ.Mismatch.No. -0.02360 0.97668 0.03926 -0.601 0.548
##
## exp(coef) exp(-coef) lower .95 upper .95
## DQ.Mismatch.No. 0.9767 1.024 0.9044 1.055
##
## Concordance= 0.562 (se = 0.097 )
## Likelihood ratio test= 0.36 on 1 df, p=0.5
## Wald test = 0.36 on 1 df, p=0.5
## Score (logrank) test = 0.36 on 1 df, p=0.5
##
##
##
## $age
## $age$Model
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## coef exp(coef) se(coef) z p
## age -0.01713 0.98302 0.02255 -0.76 0.448
##
## Likelihood ratio test=0.52 on 1 df, p=0.4694
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## $age$Summary
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## age -0.01713 0.98302 0.02255 -0.76 0.448
##
## exp(coef) exp(-coef) lower .95 upper .95
## age 0.983 1.017 0.9405 1.027
##
## Concordance= 0.632 (se = 0.088 )
## Likelihood ratio test= 0.52 on 1 df, p=0.5
## Wald test = 0.58 on 1 df, p=0.4
## Score (logrank) test = 0.58 on 1 df, p=0.4plot_quartiles_and_logrank(df, independent_vars, "X1st.DSA_status", "X1st.DSA_time")## Log-rank test result for A.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## A.Mismatch.No.=1 9 9 9.39 0.0165 0.0422
## A.Mismatch.No.=2 8 8 7.61 0.0204 0.0422
##
## Chisq= 0 on 1 degrees of freedom, p= 0.8
## Log-rank test result for B.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## B.Mismatch.No.=1 8 8 9.93 0.374 0.955
## B.Mismatch.No.=2 9 9 7.07 0.526 0.955
##
## Chisq= 1 on 1 degrees of freedom, p= 0.3
## Log-rank test result for C.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## C.Mismatch.No.=1 8 8 5.1 1.652 2.79
## C.Mismatch.No.=2 9 9 11.9 0.708 2.79
##
## Chisq= 2.8 on 1 degrees of freedom, p= 0.1
## Log-rank test result for DRB1.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DRB1.Mismatch.No.=1 9 9 8.81 0.00405 0.00917
## DRB1.Mismatch.No.=2 8 8 8.19 0.00436 0.00917
##
## Chisq= 0 on 1 degrees of freedom, p= 0.9
## Log-rank test result for DRB345.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DRB345.Mismatch.No.=1 9 9 8.53 0.0263 0.0582
## DRB345.Mismatch.No.=2 8 8 8.47 0.0265 0.0582
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.8
## Log-rank test result for DP.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DP.Mismatch.No.=1 8 8 6.61 0.292 0.536
## DP.Mismatch.No.=2 9 9 10.39 0.186 0.536
##
## Chisq= 0.5 on 1 degrees of freedom, p= 0.5
## Log-rank test result for DQ.Mismatch.No. :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DQ.Mismatch.No.=1 6 6 5.71 0.01479 0.0235
## DQ.Mismatch.No.=2 11 11 11.29 0.00748 0.0235
##
## Chisq= 0 on 1 degrees of freedom, p= 0.9
## Log-rank test result for age :
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## age=1 10 10 8.23 0.380 0.807
## age=2 7 7 8.77 0.357 0.807
##
## Chisq= 0.8 on 1 degrees of freedom, p= 0.4
## $A.Mismatch.No.
## $A.Mismatch.No.$plot
##
## $A.Mismatch.No.$log_rank_result
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## A.Mismatch.No.=1 9 9 9.39 0.0165 0.0422
## A.Mismatch.No.=2 8 8 7.61 0.0204 0.0422
##
## Chisq= 0 on 1 degrees of freedom, p= 0.8
##
##
## $B.Mismatch.No.
## $B.Mismatch.No.$plot
##
## $B.Mismatch.No.$log_rank_result
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## B.Mismatch.No.=1 8 8 9.93 0.374 0.955
## B.Mismatch.No.=2 9 9 7.07 0.526 0.955
##
## Chisq= 1 on 1 degrees of freedom, p= 0.3
##
##
## $C.Mismatch.No.
## $C.Mismatch.No.$plot
##
## $C.Mismatch.No.$log_rank_result
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## C.Mismatch.No.=1 8 8 5.1 1.652 2.79
## C.Mismatch.No.=2 9 9 11.9 0.708 2.79
##
## Chisq= 2.8 on 1 degrees of freedom, p= 0.1
##
##
## $DRB1.Mismatch.No.
## $DRB1.Mismatch.No.$plot
##
## $DRB1.Mismatch.No.$log_rank_result
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DRB1.Mismatch.No.=1 9 9 8.81 0.00405 0.00917
## DRB1.Mismatch.No.=2 8 8 8.19 0.00436 0.00917
##
## Chisq= 0 on 1 degrees of freedom, p= 0.9
##
##
## $DRB345.Mismatch.No.
## $DRB345.Mismatch.No.$plot
##
## $DRB345.Mismatch.No.$log_rank_result
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DRB345.Mismatch.No.=1 9 9 8.53 0.0263 0.0582
## DRB345.Mismatch.No.=2 8 8 8.47 0.0265 0.0582
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.8
##
##
## $DP.Mismatch.No.
## $DP.Mismatch.No.$plot
##
## $DP.Mismatch.No.$log_rank_result
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DP.Mismatch.No.=1 8 8 6.61 0.292 0.536
## DP.Mismatch.No.=2 9 9 10.39 0.186 0.536
##
## Chisq= 0.5 on 1 degrees of freedom, p= 0.5
##
##
## $DQ.Mismatch.No.
## $DQ.Mismatch.No.$plot
##
## $DQ.Mismatch.No.$log_rank_result
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## DQ.Mismatch.No.=1 6 6 5.71 0.01479 0.0235
## DQ.Mismatch.No.=2 11 11 11.29 0.00748 0.0235
##
## Chisq= 0 on 1 degrees of freedom, p= 0.9
##
##
## $age
## $age$plot
##
## $age$log_rank_result
## Call:
## survdiff(formula = fit_formula, data = data)
##
## n=17, 81 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## age=1 10 10 8.23 0.380 0.807
## age=2 7 7 8.77 0.357 0.807
##
## Chisq= 0.8 on 1 degrees of freedom, p= 0.4plot density curves
plot_density_faceted <- function(data, colnames) {
# Check if all colnames exist in the dataframe
missing_cols <- colnames[!colnames %in% names(data)]
if(length(missing_cols) > 0) {
stop("The following columns are missing from the dataframe: ", paste(missing_cols, collapse = ", "), ".")
}
# Melt the dataframe to long format for faceting
long_data <- reshape2::melt(data, measure.vars = colnames)
# Create the density plot
p <- ggplot(long_data, aes(x = value)) +
geom_density(aes(fill = variable), alpha = 0.5) +
facet_wrap(~ variable, scales = "free") +
labs(x = "Value", y = "Density") +
theme_minimal()
svg("figs/density_plot.svg")
print(p)
dev.off()
}
plot_density_faceted(df, HLA_vars)## png
## 2plot_density_faceted(df, HLA_al_vars)## png
## 2DSA survival curve (eplet)
cox_results<-univariate_coxph(df, independent_al_vars, "X1st.DSA_status", "X1st.DSA_time")
##
## Cox proportional hazards model for A :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## A 0.9612 2.6149 0.5916 1.625 0.104
##
## exp(coef) exp(-coef) lower .95 upper .95
## A 2.615 0.3824 0.8201 8.337
##
## Concordance= 0.618 (se = 0.063 )
## Likelihood ratio test= 2.96 on 1 df, p=0.09
## Wald test = 2.64 on 1 df, p=0.1
## Score (logrank) test = 2.83 on 1 df, p=0.09
##
## Cox proportional hazards model for B :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## B 0.1525 1.1648 0.6644 0.23 0.818
##
## exp(coef) exp(-coef) lower .95 upper .95
## B 1.165 0.8585 0.3168 4.283
##
## Concordance= 0.559 (se = 0.044 )
## Likelihood ratio test= 0.05 on 1 df, p=0.8
## Wald test = 0.05 on 1 df, p=0.8
## Score (logrank) test = 0.05 on 1 df, p=0.8
##
## Cox proportional hazards model for C :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## C -0.3245 0.7229 0.3482 -0.932 0.351
##
## exp(coef) exp(-coef) lower .95 upper .95
## C 0.7229 1.383 0.3653 1.43
##
## Concordance= 0.562 (se = 0.084 )
## Likelihood ratio test= 0.83 on 1 df, p=0.4
## Wald test = 0.87 on 1 df, p=0.4
## Score (logrank) test = 0.88 on 1 df, p=0.3
##
## Cox proportional hazards model for DRB1 :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB1 -0.06552 0.93658 0.42950 -0.153 0.879
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB1 0.9366 1.068 0.4036 2.173
##
## Concordance= 0.485 (se = 0.062 )
## Likelihood ratio test= 0.02 on 1 df, p=0.9
## Wald test = 0.02 on 1 df, p=0.9
## Score (logrank) test = 0.02 on 1 df, p=0.9
##
## Cox proportional hazards model for DRB345 :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DRB345 -0.1247 0.8827 0.1385 -0.901 0.368
##
## exp(coef) exp(-coef) lower .95 upper .95
## DRB345 0.8827 1.133 0.6729 1.158
##
## Concordance= 0.57 (se = 0.08 )
## Likelihood ratio test= 0.82 on 1 df, p=0.4
## Wald test = 0.81 on 1 df, p=0.4
## Score (logrank) test = 0.82 on 1 df, p=0.4
##
## Cox proportional hazards model for DP :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DP -0.6149 0.5407 0.3030 -2.029 0.0424 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## DP 0.5407 1.849 0.2986 0.9793
##
## Concordance= 0.614 (se = 0.094 )
## Likelihood ratio test= 4.64 on 1 df, p=0.03
## Wald test = 4.12 on 1 df, p=0.04
## Score (logrank) test = 4.38 on 1 df, p=0.04
##
## Cox proportional hazards model for DQ :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DQ -0.2390 0.7874 0.2707 -0.883 0.377
##
## exp(coef) exp(-coef) lower .95 upper .95
## DQ 0.7874 1.27 0.4633 1.338
##
## Concordance= 0.588 (se = 0.082 )
## Likelihood ratio test= 0.76 on 1 df, p=0.4
## Wald test = 0.78 on 1 df, p=0.4
## Score (logrank) test = 0.79 on 1 df, p=0.4
##
## Cox proportional hazards model for age :
## Call:
## coxph(formula = fit_formula, data = data, na.action = na.exclude)
##
## n= 17, number of events= 17
## (81 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## age -0.01713 0.98302 0.02255 -0.76 0.448
##
## exp(coef) exp(-coef) lower .95 upper .95
## age 0.983 1.017 0.9405 1.027
##
## Concordance= 0.632 (se = 0.088 )
## Likelihood ratio test= 0.52 on 1 df, p=0.5
## Wald test = 0.58 on 1 df, p=0.4
## Score (logrank) test = 0.58 on 1 df, p=0.4
##
## Variable HR Lower_CI Upper_CI p_value C_index
## A A 2.6148671 0.8201377 8.3370509 0.10420631 0.6176471
## B B 1.1647798 0.3167629 4.2830516 0.81841013 0.5588235
## C C 0.7228977 0.3653291 1.4304392 0.35139506 0.5625000
## DRB1 DRB1 0.9365844 0.4036056 2.1733848 0.87876111 0.4852941
## DRB345 DRB345 0.8827405 0.6729353 1.1579580 0.36770740 0.5698529
## DP DP 0.5407217 0.2985717 0.9792619 0.04244575 0.6139706
## DQ DQ 0.7874395 0.4632733 1.3384345 0.37727129 0.5882353
## age age 0.9830200 0.9405224 1.0274379 0.44754506 0.6323529